Canadian Triage and Acuity Scale: testing the mental health categories
Bibliographic record
Abstract
PURPOSE: The study tested the inter-rater reliability and accuracy of triage nurses' assignment of urgency ratings for mental health patient scenarios based on the 2008 Canadian Triage and Acuity Scale (CTAS) guidelines, using a standardized triage tool. The influence of triage experience, educational preparation, and comfort level with mental health presentations on the accuracy of urgency ratings was also explored. METHODS: Study participants assigned urgency ratings to 20 mental health patient scenarios in randomized order using the CTAS. The scenarios were developed using actual triage notes and were reviewed by an expert panel of emergency and mental health clinicians for face and content validity. RESULTS: The overall Fleiss' kappa, the measure of inter-rater reliability for this sample of triage nurses (n=18), was 0.312, representing only fair albeit statistically significant (P<0.0001) agreement. Kendall's coefficient of concordance for the sample was calculated to be 0.680 (P<0.0001), which signifies moderate agreement. Although the sample reported high levels of education, comfort with mental health presentations, and experience, accuracy in urgency ratings measured by the percentage of correct responses ranged from 0.05% to 94% (mean: 54%). Greater accuracy in urgency ratings was recorded for triage nurses who used second-order modifiers and avoided the use of override. CONCLUSION: Specific focus on the use of second-order modifiers in orientation and ongoing education of triage nurses may improve the reliability and validity of the CTAS when used to assign urgency ratings to mental health presentations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".